A Method for Fast Autonomy Transfer in Reinforcement Learning
This addresses the need for efficient adaptation in RL applications, though it appears incremental as it builds on existing actor-critic methods.
The paper tackled the problem of slow autonomy transfer in reinforcement learning by introducing the Multi-Critic Actor-Critic (MCAC) algorithm, which achieved up to 22.76x faster transfer and higher reward accumulation compared to baseline methods.
This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings without requiring extensive computational resources. Our contributions include development of the Multi-Critic Actor-Critic (MCAC) algorithm, establishing its convergence, and empirical evidence demonstrating its efficacy. Our experimental results show that MCAC significantly outperforms the baseline actor-critic algorithm, achieving up to 22.76x faster autonomy transfer and higher reward accumulation. This advancement underscores the potential of leveraging accumulated knowledge for efficient adaptation in RL applications.